Location awareness is one of the fundamental characteristics of cognitive radio (CR) technology. Realization of location awareness requires incorporation of a location information management system into cognitive radios and/or networks. In [1], a system model for location information management in cognitive wireless networks is introduced and it is extended and referred as location awareness engine in [2]. Both [1], [2] provide numerous application of location awareness in cognitive radios as well as wireless networks. These applications are fourfold; location-based services (LBSs), location-assisted network optimization, environment characterization [3], and transceiver algorithm optimization. Various detailed applications for each of these four categories are provided in [1]-[3].
Applications of location awareness can require different level of positioning accuracy. For instance, generally, indoor positioning systems demand higher precision accuracy compared to outdoor positioning systems. More specifically, asset management in industrial areas, which is a local positioning application, can require typically 0.05-30 m accuracy depending on the specific applications [4]. On the other hand, E911 services require 50-300 m accuracy in most cases [5]. For instance, when a CR device is located in the outdoor environment (e.g. in a public park), CR can adjust its accuracy level to 100 m to satisfy E911 services requirements in the United States. In this case, it can be assumed that the current waveform of the CR is GSM and the CR user leaves from the park to home. After entering to the home, the CR device recognizes the WLAN network at home using its interoperability capability and it switches its waveform to WLAN. Consequently, it can improve its positioning accuracy (e.g. 5 m) since it operates in the indoor environment. In order to support different location awareness based applications using CRs, an adaptive positioning system that can achieve accuracy adaptation in both indoor and outdoor environments is required.
To the best of inventors' knowledge, there is no solid study in the literature on the evaluation and comparison of the existing positioning technologies (e.g. GPS, UWB positioning) in the light of realization of location awareness in CRs. This issue is a current research topic since the accuracy and complexity of the employed positioning technique can affect the performance of the location awareness related applications. However, in this specification, a brief discussion on some of the existing positioning technologies such as GPS and UWB positioning is provided. There are different forms of GPS technology; standard GPS (4-20 m accuracy), Code-Phase GPS (3-6 m accuracy), Carrier-Phase GPS (3-4 mm accuracy), Differential GPS (sub-decimeter) [6], Assisted-GPS (less than 10 m accuracy) [7], Indoor GPS [8], and Software GPS [9]. As it can be seen that each of these GPS technologies provides a different level of accuracy. Even combining these different forms of GPS in a single device to provide switched accuracy level (not adaptive) is impractical and costly. However, software GPS is a promising method to switch between different GPS forms. But, eventually, this approach will only provide a set of fixed accuracy levels that are provided by each form of GPS. Basically, the existing GPS technologies do not have a capability to achieve accuracy adaptation. Moreover, GPS is not a low-cost and low-power solution [10] for some wireless networks (e.g. wireless sensor networks) where the cost and power are the major concerns.
Another alternative technology is UWB positioning, which has the capability to provide centimeter ranging accuracy due to the use of large bandwidth during the transmission [11]. However, this technology does not have a capability to achieve accuracy adaptation either. Moreover, this technology provides such fixed and high-precision positioning accuracy within only short ranges. In [12], a hybrid distance estimation technique for a legacy positioning system that is based on time-of arrival (TOA) and signal strength methods is disclosed. The technique provides flexibility to improve the accuracy using a priori distance information rather than achieving accuracy adaptation.
The details of the existing location estimation (e.g. triangulation, proximity) and sensing (e.g. scene analysis) techniques in CR context are presented in [2]. Moreover, the details of some specific location estimation techniques such as TOA for CRs are provided in [13]. However, according to [2], legacy positioning techniques without enhancements do not provide the required cognition capability that a CR demands. As a result, deficiencies of the existing legacy positioning systems in terms of providing cognition features such as accuracy adaptation in our case motivate us to develop a cognitive positioning system (CPS). TOA, signal strength, and angle-of-arrival (AOA) legacy location estimation techniques can be considered as candidates for the disclosed CPS, if they can be enhanced with cognition capabilities. AOA techniques are mostly implemented by means of antenna arrays. But, angulation employing antenna arrays is not suitable for rich multipath environments such as indoor UWB propagation channel due to the cost and imprecise location estimation [14]. On the other hand, signal strength based methods provide high accuracy only for the short ranges since the Cramer-Rao Lower Bound (CRLB) for these methods depend on the distance [12]. Moreover, the performance of the estimator for signal strength techniques depends on the channel parameters such as the path loss factor and standard deviation of the shadowing effects. Additionally, CR does not have much control over the channel parameters but to measure them in order to adjust the accuracy. Since the accuracy of TOA techniques mainly depends on the parameter that transceiver can control, it is the most suitable location estimation technique for the CPS. Therefore, the legacy TOA technique is improved and referred as adaptive-TOA (A-TOA) in this specification. This technique is adopted for the CPS in order to determine the required effective bandwidth and consequently to estimate the location information. Dynamic spectrum management (DSM) in CR technology can be used for both communications [15] and positioning systems. However, the performance and optimization requirements for both systems can be different. For instance, one of the main performance parameters in the communications systems is data rate, whereas it is accuracy in the positioning systems.
Similarly, the optimization algorithm that is used by DSM for the communications and positioning systems can be different. The optimization algorithm used by DSM to support positioning systems is referred as enhanced dynamic spectrum management (EDSM) in this specification. The disclosed CPS allows CR to adjust the positioning accuracy adaptively in both indoor and outdoor environments. This technique is composed of two modes, which are bandwidth determination and EDSM.
In the first mode, CPS determines the required effective bandwidth for a given accuracy. A-TOA estimation technique is used in this mode. The required effective bandwidth is determined using the bandwidth determination equation, which is derived through CRLB for both additive white Gaussian noise (AWGN) and multipath channels in this specification. Once the effective bandwidth is determined, the second mode that is the EDSM system is initiated. The main responsibility of the EDSM is to search, find and provide the optimum available bandwidth to the CPS. Two EDSM schemes, which are overlay spectrum access based EDSM (O-EDSM) and hybrid overlay and underlay spectrum access based EDSM (H-EDSM) are disclosed. An algorithm for H-EDSM method that is used to switch between underlay and overlay spectrum usage modes is introduced. The switching algorithm is developed based on Two-slope (2-Ray) model. Finally, the specified relative bandwidth is used by the reference CR node to transmit signal, and a TOA based location estimation algorithm (e.g. A-TOA) is employed by the target CR node to estimate the location with given accuracy. Note that it is assumed that the reference and target CR nodes agree on the relative bandwidth during the initial ranging handshake mechanism. Moreover, simulation results and challenges related to the implementation of CPS are presented in this specification.
The specification is organized as follows: a definition of CR along with the system model is provided in Section II. In Section III, theoretical analysis for bandwidth determination in both AWGN and multipath channels through the CRLB is presented. In Section IV, the EDSM system for the CPS along with O-EDSM and H-EDSM schemes are discussed. Simulation results and implementation challenges of the disclosed CPS are presented in Section V. The remarkable conclusions and further studies are outlined in Section VI.